This application relates to an image acquisition and image processing technique where the image is captured utilizing a spatially pseudo-randomly ordered filter.
Image reconstruction is inherent in any number of technical areas. As an example, surveillance aircraft capture images at multiple wavelengths which must be reconstructed to provide information. These images must be captured relatively quickly, and the accuracy, spatial resolution, and dynamic range must be as high as possible.
To date, images have generally been captured with a sensor array (a camera) provided with a filter. The filter breaks the incoming light into different levels. In one example, the different levels may be intensity levels, although different spectral levels (the colors red, blue, green, for instance) may also be separated by the filter. It is also known to use different filters to separate other qualities within the light.
To date, these filters have generally been provided in a regular pattern for light in the human-visible portion of the electromagnetic spectrum. There is no inherent restriction, however, to extending these filters to other portions of the electromagnetic or acoustic spectrums or properties of electromagnetic or acoustic waves.
As an example, in FIG. 1, a sensor focal plane array (FPA) 20 is illustrated having a spatially regular mask 22 with two different intensity attenuation levels 24, 26. The mask 22 is shown detached from FPA 20 for illustration purposes; typically these are attached to each other. The light rays 28, 30 from scene 32 are filtered by the different intensity attenuation levels 24, 26 resulting in a patchwork image 34. As can be appreciated, the different intensities in the resulting images are regularly spaced or geometrically ordered, corresponding to the spatial regularity of mask 22. If filter levels 24, 26 comprise bandpass or bandstop filters for different wavelengths, then the resulting images are geometrically ordered by wavelength. If filter levels 24, 26 comprise different polarizations, then the resulting images are geometrically ordered by polarization, etc. It is known in the art that patchwork image 34 may be reconstructed into a high dynamic range (HDR) image by a reconstruction technique that includes interpolation.
There are intrinsic problems for this imaging scheme. Due to the grid-like sampling, the image is reconstructed by interpolation, which usually results in loss of spatial resolution and/or loss of high spatial frequency components of the images.
Alternatives to the described high dynamic range (HDR) imaging using an intensity attenuation mask include taking multiple exposures of different exposure times, using multiple sensors and beam splitters, or fabricating an FPA with multiple sized pixels. The first alternative suffers image degradation especially for moving objects; the second alternative suffers increased cost; the third alternative suffers decreased spatial resolution.
Some literature describing image processing techniques has indicated that the filters need not be ordered or regularly spaced, and that some reconstruction techniques may work with random filter arrangements. However, there is no benefit or instruction to utilize such a random filter in this literature.